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feat(frontend): migrate evaluator playground to workflow evaluators #3767
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mmabrouk
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docs/migrate-evaluator-playground-plan
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feat/migrate-evaluator-playground-frontend
Feb 16, 2026
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e5a0e16
docs: add evaluator playground migration planning workspace
mmabrouk df1e622
docs: update plan to direct migration (no adapters), split into PR 1 …
mmabrouk e3e633d
feat(frontend): migrate evaluator configs CRUD
mmabrouk 02ad4dc
fix(frontend): remove duplicate hook imports
mmabrouk 9b9435a
feat(frontend): invoke evaluators via workflows
mmabrouk 8ecf91c
fix(frontend): use explicit evaluator URI for invoke
mmabrouk 23e6d62
fix(frontend): harden evaluator invoke interface
mmabrouk 8495417
Merge branch 'chore/migrate-evaluators' into feat/migrate-evaluator-p…
mmabrouk dfcd091
Merge branch 'feat/migrate-evaluator-playground-frontend' into feat/m…
mmabrouk 00c2aa2
fix(frontend): exclude container ag metrics from overview
mmabrouk bef12ec
Revert "fix(frontend): exclude container ag metrics from overview"
mmabrouk fa91fcf
fix(api): hydrate builtin evaluator schemas on simple CRUD
mmabrouk 09dba15
fix(evaluators): persist template-driven output schemas
mmabrouk 0c6ae55
chore(api): apply ruff formatting cleanup
mmabrouk 2daf78d
Merge pull request #3577 from Agenta-AI/feat/migrate-evaluator-playgr…
mmabrouk 9426708
Merge remote-tracking branch 'origin/docs/migrate-evaluator-playgroun…
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🟡 Shallow merge in
_ensure_builtin_evaluator_datacan silently discard hydrated output schemasWhen the existing
simple_evaluator_datahas aschemasfield set to a value that does not contain anoutputskey (e.g.,schemas: {}orschemas: {"inputs": {...}}), the shallow dict merge{**hydrated_data_dict, **existing_data_dict}completely replaces the hydratedschemas(which contains the computedoutputs) with the existing incompleteschemas, thereby losing the output schema the function was supposed to inject.Root Cause & Impact
The function's purpose is to fill in missing output schemas for builtin evaluators. At
api/oss/src/core/evaluators/service.py:848-853, the merge is:Because Python dict unpacking is a shallow merge, the entire
schemaskey fromexisting_data_dictreplaces the one fromhydrated_data_dict. For example:hydrated_data_dict["schemas"] = {"outputs": {"type": "object", ...}}existing_data_dict["schemas"] = {}(or{"inputs": {...}})schemas = {}— the outputs schema is lostThe
_has_outputs_schemaguard at line 817 only checks if outputs already exist and returns early. It does not prevent the merge from discarding the hydrated outputs whenschemasexists but lacks theoutputskey.Impact: Evaluator revisions can be persisted without the expected
data.schemas.outputs, which may break downstream consumers that rely on knowing the evaluator's output shape (e.g., workflow invocation, result rendering).Prompt for agents
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